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ORCIDDavid C. Kale
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- affiliation: University of California, Los Angeles, Department of Computer Science
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Journal Articles
- 2017
[j1]Ryan J. Gallagher, Kyle Reing, David C. Kale, Greg Ver Steeg:
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge. Trans. Assoc. Comput. Linguistics 5: 529-542 (2017)
Conference and Workshop Papers
- 2019
- [c17]Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah:
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data. PSB 2019: 18-29 - 2017
- [c16]Sébastien Dubois, Nathanael Romano, Kenneth Jung, Nigam Shah, David C. Kale:
The Effectiveness of Transfer Learning in Electronic Health Records Data. ICLR (Workshop) 2017 - [c15]Tom Quisel, Luca Foschini
, Alessio Signorini, David C. Kale:
Collecting and Analyzing Millions of mHealth Data Streams. KDD 2017: 1971-1980 - 2016
- [c14]Zachary C. Lipton, David C. Kale, Randall C. Wetzel:
Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series. MLHC 2016: 253-270 - [c13]Zachary Chase Lipton, David C. Kale, Charles Elkan, Randall C. Wetzel:
Learning to Diagnose with LSTM Recurrent Neural Networks. ICLR (Poster) 2016 - 2015
- [c12]David C. Kale, Zhengping Che, Mohammad Taha Bahadori, Wenzhe Li, Yan Liu, Randall C. Wetzel:
Causal Phenotype Discovery via Deep Networks. AMIA 2015 - [c11]Mohammad Taha Bahadori, David C. Kale, Yingying Fan, Yan Liu:
Functional Subspace Clustering with Application to Time Series. ICML 2015: 228-237 - [c10]Zhengping Che, David C. Kale, Wenzhe Li, Mohammad Taha Bahadori, Yan Liu:
Deep Computational Phenotyping. KDD 2015: 507-516 - [c9]David C. Kale, Marjan Ghazvininejad, Anil Ramakrishna, Jingrui He, Yan Liu:
Hierarchical Active Transfer Learning. SDM 2015: 514-522 - 2014
- [c8]Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens:
Preface. AAAI Workshop: Modern Artificial Intelligence for Health Analytics 2014 - [c7]David C. Kale, Dian Gong, Zhengping Che, Yan Liu, Gérard G. Medioni, Randall C. Wetzel, Patrick Ross:
An Examination of Multivariate Time Series Hashing with Applications to Health Care. ICDM 2014: 260-269 - 2013
- [c6]David C. Kale, Samuel Di, Yan Liu, Yolanda Gil:
Capturing Data Analytics Expertise with Visualizations in Workflows. AAAI Fall Symposia 2013 - [c5]David C. Kale, Yan Liu:
Accelerating Active Learning with Transfer Learning. ICDM 2013: 1085-1090 - 2012
- [c4]Brendan Stubbs, David C. Kale:
Sim•TwentyFive: An Interactive Visualization System for Data-Driven Decision Support. AMIA 2012 - [c3]Benjamin M. Marlin, David C. Kale, Robinder G. Khemani, Randall C. Wetzel:
Unsupervised pattern discovery in electronic health care data using probabilistic clustering models. IHI 2012: 389-398 - 2011
- [c2]Daniel J. Crichton, Chris A. Mattmann, Andrew F. Hart, David C. Kale, Robinder G. Khemani, Patrick Ross, Sarah Rubin, Paul Veeravatanayothin, Amy Braverman, Cameron Goodale, Randall C. Wetzel:
An informatics architecture for the Virtual Pediatric Intensive Care Unit. CBMS 2011: 1-6 - 2009
- [c1]David C. Kale, David G. Stork:
Estimating the position of illuminants in paintings under weak model assumptions: an application to the works of two Baroque masters. Human Vision and Electronic Imaging 2009: 72401
Parts in Books or Collections
- 2017
- [p1]Zhengping Che, Sanjay Purushotham, David C. Kale, Wenzhe Li, Mohammad Taha Bahadori, Robinder G. Khemani, Yan Liu:
Time Series Feature Learning with Applications to Health Care. Mobile Health - Sensors, Analytic Methods, and Applications 2017: 389-409
Editorship
- 2020
- [e6]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2020, 7-8 August 2020, Virtual Event, Durham, NC, USA. Proceedings of Machine Learning Research 126, PMLR 2020 [contents] - 2019
- [e5]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, 9-10 August 2019, Ann Arbor, Michigan, USA. Proceedings of Machine Learning Research 106, PMLR 2019 [contents] - 2018
- [e4]Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2018, 17-18 August 2018, Palo Alto, California. Proceedings of Machine Learning Research 85, PMLR 2018 [contents] - 2017
- [e3]Finale Doshi-Velez, Jim Fackler, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Health Care Conference, MLHC 2017, Boston, Massachusetts, USA, 18-19 August 2017. Proceedings of Machine Learning Research 68, PMLR 2017 [contents] - 2016
- [e2]Finale Doshi-Velez, Jim Fackler, David C. Kale, Byron C. Wallace, Jenna Wiens:
Proceedings of the 1st Machine Learning in Health Care, MLHC 2016, Los Angeles, CA, USA, August 19-20, 2016. JMLR Workshop and Conference Proceedings 56, JMLR.org 2016 [contents] - 2014
- [e1]Finale Doshi-Velez, David C. Kale, Byron C. Wallace, Jenna Wiens:
Modern Artificial Intelligence for Health Analytics, Papers from the 2014 AAAI Workshop, Québec City, Québec, Canada, July 27, 2014. AAAI Technical Report WS-14-08, AAAI 2014 [contents]
Informal and Other Publications
- 2018
- [i8]Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah:
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data. CoRR abs/1808.03331 (2018) - 2017
- [i7]Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, Aram Galstyan:
Multitask Learning and Benchmarking with Clinical Time Series Data. CoRR abs/1703.07771 (2017) - 2016
- [i6]Zachary Chase Lipton, David C. Kale, Randall C. Wetzel:
Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series. CoRR abs/1606.04130 (2016) - [i5]Kyle Reing, David C. Kale, Greg Ver Steeg, Aram Galstyan:
Toward Interpretable Topic Discovery via Anchored Correlation Explanation. CoRR abs/1606.07043 (2016) - [i4]Ryan J. Gallagher, Kyle Reing, David C. Kale, Greg Ver Steeg:
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge. CoRR abs/1611.10277 (2016) - [i3]Tom Quisel, David C. Kale, Luca Foschini:
Intra-day Activity Better Predicts Chronic Conditions. CoRR abs/1612.01200 (2016) - 2015
- [i2]Bharath Sankaran, Marjan Ghazvininejad, Xinran He, David C. Kale, Liron Cohen:
Learning and Optimization with Submodular Functions. CoRR abs/1505.01576 (2015) - [i1]Zachary Chase Lipton, David C. Kale, Randall C. Wetzel:
Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks. CoRR abs/1510.07641 (2015)
Coauthor Index
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